TOPFARM: Multi‐fidelity optimization of wind farms. (15th November 2013)

• Record Type: Journal Article
• Authors: Réthoré, Pierre‐Elouan; Fuglsang, Peter; Larsen, Gunner C.; Buhl, Thomas; Larsen, Torben J.; Madsen, Helge A.
• Is Part Of: Wind energy. Volume 17:Number 12(2014)
• Page Start: 1797
• Page End: 1816
• Publication Date: 2013-11-15
• Subjects: Wind power -- Periodicals; 621.312136
• DOI: 10.1002/we.1667
• Languages: English
• ISSNs: 1095-4244

Abstract

<abstract abstract-type="main" id="we1667-abs-0001"> <title>ABSTRACT</title> <p>A wind farm layout optimization framework based on a multi‐fidelity optimization approach is applied to the offshore test case of Middelgrunden, Denmark as well as to the onshore test case of Stag Holt – Coldham wind farm, UK. While aesthetic considerations have heavily influenced the famous curved design of the Middelgrunden wind farm, this work focuses on demonstrating a method that optimizes the profit of wind farms over their lifetime based on a balance of the energy production income, the electrical grid costs, the foundations cost, and the cost of wake turbulence induced fatigue degradation of different wind turbine components. A multi‐fidelity concept is adapted, which uses cost function models of increasing complexity (and decreasing speed) to accelerate the convergence to an optimum solution. In the EU‐FP6 TOPFARM project, three levels of complexity are considered. The first level uses a simple stationary wind farm wake model to estimate the Annual Energy Production (AEP), a foundations cost model depending on the water depth and an electrical grid cost function dictated by cable length. The second level calculates the AEP and adds a wake‐induced fatigue degradation cost function on the basis of the interpolation in a database of simulations performed for various wind speeds and wake setups with the aero‐elastic code HAWC2 and the dynamic wake meandering model. The third level, not considered in this present paper, includes directly the HAWC2 and the dynamic wake meandering model in the optimization loop in order to estimate both the fatigue costs and the AEP. The novelty of this work is the implementation of the multi‐fidelity approach in the context of wind farm optimization, the inclusion of the fatigue degradation costs in the optimization framework, and its application on the optimal performance as seen through an economical perspective. Copyright © 2013 John Wiley & Sons, Ltd.</p> </abstract>